Ecological constraints on language diversity and evolution
Christophe Coupé
Laboratoire Dynamique du Langage (UMR 5596 – CNRS / Université Lyon 2)
Understanding why languages are what they are and how they change requires an understanding of the many physical substrates which support them. Although it is possible to focus on internal constraints organizing linguistic systems, along paradigmatic and syntagmatic dimensions, without explicitly referring to these substrates, linguists have gradually explored issues such as:
i) how the human production and perceptual systems bear an impact on the sound systems of the world’s languages, with opposite constraints of ease of articulation and ease of perception (e.g. Lindblom et al., 1984; Oudeyer 2006);
ii) how our cognition shapes linguistic semantics and syntax (e.g. Talmy, 2000);
iii) how our genetic material (Pinker & Bloom, 1990), or more recently specific genes (Lai et al., 2001), relate to the physiological and cognitive features behind the use of language;
iv) how the intricacies of the web of social ties between speakers partly explain language variability and change (Labov, 2001; Milroy & Milroy, 1998; Nettle, 1999; Ke et al., 2008).
Aside from these factors, a range of more distant elements related to the physical environment inhabited by our species call attention. The expression “ecology of language” has been coined by Mufwene (2001) to refer to the diverse social frames in which languages change and compete – for example to give birth to creoles; it is tempting however to consider the term “ecology” in its more common acceptance, and to investigate whether factors such as climate or topography may influence linguistic systems.
A somehow direct impact of such physical parameters can be found for example in the linguistic and gestural representations of time in hilly environments such as the Yupno Valley of Papua New Guinea, where locals refer to past and future as down or up the slope (Núñez et al., 2012). Some scholars have also questioned the effect of topography or tree coverage on phonetic systems on the basis of sound transmissibility, with theories such as the acoustic adaptation hypothesis (Maddieson, 2011). A more indirect influence of ecological factors on linguistic variables can however be considered given i) how the first may lead to specific social configurations, and ii) how these configurations may in turn impact on linguistic features. The disputed relationships between group size and either phoneme inventory size (Pericliev, 2004; Hay & Bauer, 2007;
Bybee, 2011) or language complexity/structure (Lupyan & Dale, 2010; Dahl, 2011) illustrate the second implication, i.e. how a social dimension may be connected to linguistic phenomena. Bridging the gap with environmental considerations, Nettle demonstrated how ecological risk in equatorial Africa reduced linguistic diversity. He explained this trend by showing that ecological risk, which is closely related to the duration of the growing periods for plants, leads people to enter into long-distance relationships to reduce the consequences of severe ecological problems like droughts (Nettle, 1996; 1998). Jacquesson (2001, 2003) also attempted at relating rates of linguistic change to specific environmental areas like ‘quasi-deserts’.
An outcome of such studies is a better understanding of macro- or meso- patterns of current linguistic diversity. These studies may also inform the way we address past linguistic situations, and the dilations and contractions different languages went through before reaching the linguistic landscape we know today. This requires the possibility to push back in time the current “ecology” of languages; such an assumption seems however reasonable at least for the last hundreds of years, provided differences between various modes of subsistence are not left out of the equation (like farming, raising animals, hunting and gathering).
Following Nettle’s pioneering approach, we rely on a nomothetic approach to investigate the relationship between ecological variables and social and linguistic ones. More precisely, we take advantage of the ever growing body of high-resolution digital datasets to investigate the impact of i) elevation and ground rugosity (Amante & Eakins, 2009), ii) distance to fresh water (Kummu et al., 2011), iii) tree and herbaceous coverage (Hansen et al. 2003), iv) duration of the growing season (FAO & IIASA, 2000) on a) population density (CIESIN &
CIAT, 2005) and b) number of speakers per language and spatial quantitative measures of linguistic diversity (GMI & SIL, 2012). Relying on a geographic information system, we conduct statistical analyses of the relationships between the previous variables within regularly distributed geographic cells.
We first applied our approach to the African continent (Coupé & Hombert, 2012). Using stepwise linear regressions, we especially found that the density of population, as well as the number of languages in a region
and the size of their geographic areas, could be well predicted by ecological factors. Additionally, regions where the linguistic diversity was the highest above the prediction interestingly corresponded to the putative cradle of the Bantu languages (Ehret, 2001). Finally, we also confirmed and expanded Nettle’s conclusions regarding ecological risk and the duration of the growing period.
We are now conducting similar investigations for the East Asian region. We will present similar analyses as those previously described regarding the sociolinguistic features of the region, and will compare this context to the African one. We will also pay closer attention to the issue of dealing with non-independent statistical units (because of the spatial contiguity of the cells in which measurements of the variables are done) (following Jaeger et al., 2011). Finally, we will attempt at discussing past linguistic evolution in the Eastern Asian region in the light of available palaeoclimatic data (e.g. Chen, 2008).
(887 words)
References:
• Amante, C. & Eakins, B. W. (2009). ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis.
NOAA Technical Memorandum NESDIS NGDC-24.
• Bybee, J. (2011). How plausible is the hypothesis that population size and dispersal are related to phoneme inventory size? Introducing and commenting on a debate. Linguistic Typology 15(2): 147-153.
• Chen, X. (2008). Paleoclimate of China. In Fu et al. (eds.), Regional Climate Studies of China. Springer Verlag.
• Coupé, C. & Hombert, J.-M. (2012). Impact of climatic and ecological contexts on sociolinguistic factors among Bantu populations. Workshop “Impact of a major environmental crisis on species, populations and communities: the fragmentation of African forests at the end of the Holocene”. Paris, March 1-2.
• Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3): Population Density Grids.
Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw. (2011).
• Dahl, O. (2011). Are small languages more or less complex than big ones? Linguistic Typology 15(2): 171-175.
• Ehret, C. (2001). Bantu Expansions: Re-Envisioning a Central Problem of Early African History. The International Journal of African Historical Studies 34(1): 5-41.
• Food and Agriculture Organization & International Institute for Applied Systems Analysis (2000). Length of growing period, 1901-1996. Global agro-ecological zones. In FAO & IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability (H. von Velthuizen et al.).
• Global Mapping International & SIL International (2012). World Language Mapping System. Language area and point data for Geographic Information Systems (GIS). http://www.worldgeodatasets.com/language/
• Hansen, M., R. DeFries, J.R. Townshend, M. Carroll, C. Dimiceli, and R. Sohlberg (2003). Vegetation Continuous Fields MOD44B, 2001 Percent Tree Cover, Collection 3, University of Maryland, College Park, Maryland, 2001.
• Hay, J. & Bauer, L. (2007). Phoneme inventory size and population size. Language 83: 388-400.
• Jacquesson, F. (2001). Pour une linguistique des quasi-déserts. In A.M. Loffler-Laurian (ed.), Etude de linguistique générale et contrastive. Hommage à Jean Perrot. Paris : Centre de Recherche sur les Langues et les Sociétés, pp. 199- 216.
• Jacquesson, F. (2003). Linguistique, génétique et la vitesse d'évolution des langues. Bulletin de la Société de Linguistique de Paris 98(1): 101-122.
• Jaeger, T.F., Graff, P., Croft, W. & Pontillo, D. (2011). Mixed effects models for genetic and areal dependencies in linguistic typology. Linguistic Typology 15(2): 281-320.
• Ke, J., Gong, T. & Wang, W. S.-Y. (2008). Language change and social networks. Communications in Computational Physics 3(4): 935–949.
• Kummu, M., de Moel, H., Ward, P. J., Varis, O. (2011). How Close Do We Live to Water? A Global Analysis of Population Distance to Freshwater Bodies. PLoS ONE 6(6): e20578. doi:10.1371/journal. pone.0020578
• Labov, W. (2001). Principles of Linguistic Change. Volume II : Social Factors, chapter 10. Social Networks, pp. 325-365.
Wiley-Blackwell.
• Lai, C. S., Fisher, S. E., Hurst, J. A., Vargha-Khadem, F. & Monaco, A. P. (2001). A forkhead-domain gene is mutated in a severe speech and language disorder. Nature 413 (6855): 519-23.
• Lindblom, B., MacNeilage, P., and Studdert-Kennedy, M. (1984). Self-organizing processes and the explanation of language universals. In Butterworth et al. (eds.), Explanations for Language Universals, pp. 181-203.
• Lupyan, G. & Dale, R. (2010). Language Structure Is Partly Determined by Social Structure. PLoS ONE 5(1):1-10.
• Maddieson, I. (2011). Phonological complexity and linguistic patterning. Proceedings of the 17th International Congress of Phonetic Sciences. Hong Kong, China, 17-21 August 2011.
• Milroy, J. & Milroy, L. (1998). The Handbook of Sociolinguistics, chapter 3. Varieties and Variation, pp. 47–64. Wiley Blackwell.
• Mufwene, S. (2001). The ecology of language evolution. Cambridge: Cambridge University Press.
• Nettle, D. (1996). Language diversity in West Africa: An ecological approach. Journal of Anthropological Archaeology 15:403-438.
• Nettle, D. (1998). Explaining global patterns of language diversity. Journal of Anthropological Archaeology 17: 354-74.
• Nettle, D. (1999). Using social impact theory to simulate language change. Lingua 108(2-3): 95–117.
• Núñez, R., Cooperrider, K., Doan, D & Wassmann, J. (2012). Contours of Time: Topographic Construals of Past, Present, and Future in the Yupno Valley of Papua New Guinea. Cognition 124(1): 25-35.
• Oudeyer, P-Y. (2006). Self-Organization in the Evolution of Speech. Studies in the Evolution of Language. Oxford University Press. (Translation by James R. Hurford).
• Pericliev, V. (2004). There is no correlation between the size of a community speaking a language and the size of the phonological inventory of that language. Linguistic Typology 8: 376–83.
• Pinker, S., & Bloom, P. (1990). Natural Language and Natural Selection. Behavioral and Brain Sciences, 13, 707-784.
• Talmy, L. (2000). Toward a Cognitive Semantics. Cambridge, MA: MIT Press 2000.
